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Non-Sterile Zirconia Block (Model name: ARENA Star, MontBlanc) are indicated for the production of artificial teeth in fixed or removable dentures, or for jacket crowns, facings, and veneers.

Non-Sterile Zirconia Block (Model name: ARENA Star. MontBlanc), used to produce dental restoration to support designing computer for dental use and to process cutting as a manufacture unit, on which CAD/CAM system is applied for processing and sintering.

This document is a 510(k) Premarket Notification from the FDA regarding a dental device, specifically "Non-Sterile Zirconia Block (ARENA Star, Mont Blanc)". It focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study that proves the device meets specific acceptance criteria through clinical or even extensive non-clinical performance data against set thresholds.

Therefore, many of the requested details about acceptance criteria, performance studies, sample sizes, expert involvement, and ground truth establishment (typical for AI/software as a medical device submissions) are not present in this document. This submission primarily relies on showing similarity in material properties and intended use to an already cleared device.

Here's an attempt to answer your questions based solely on the provided text:

Device: Non-Sterile Zirconia Block (ARENA Star, Mont Blanc)

1. Table of acceptance criteria and the reported device performance

The document does not present a table of acceptance criteria with specific quantitative thresholds that the device was tested against in a performance study, nor does it report detailed device performance metrics beyond stating that tests "passed the evaluation criteria and met the requirement."

Instead, it relies on comparison to a predicate device and adherence to a recognized standard (ISO 6872:2015). The "acceptance criteria" are implied by meeting the requirements of this standard and demonstrating equivalence to the predicate.

Note: The document explicitly states "No clinical study is included in this submission." The reported performance is based on non-clinical, in-vitro testing.

2. Sample size used for the test set and the data provenance

The document does not specify the sample sizes (e.g., number of blocks, number of samples tested for biocompatibility, etc.) used for the non-clinical tests.

The data provenance is from non-clinical tests conducted by ARUMDENTISTRY Co., Ltd. (Republic of Korea, based on the submitter's address). The tests are in-vitro studies, not clinical data from patients. The document does not specify if the data is retrospective or prospective, but given it's non-clinical testing, it would generally be considered prospective for the purpose of this submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This question is not applicable to this submission. The "ground truth" for material properties is established through standardized laboratory testing methods (e.g., measuring flexural strength, chemical composition, biocompatibility assays), not through expert consensus or interpretation of medical images. No human readers or experts are mentioned in the context of "ground truth" for the performance tests conducted.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This question is not applicable. Adjudication methods (like 2+1, 3+1) are typically used in clinical studies involving human interpretation (e.g., radiology reads) to establish a consensus ground truth. The tests performed are objective, laboratory measurements of material properties, not subjective assessments requiring adjudication.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

This question is not applicable. This device is a dental material (zirconia block) used for manufacturing dental prosthetics, not an AI-powered diagnostic or assistive tool. Therefore, MRMC studies and assessment of AI assistance for human readers are not relevant. The document explicitly states, "No clinical study is included in this submission."

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This question is not applicable. This device is a physical dental material, not an algorithm or software. Therefore, "standalone algorithm-only performance" is not a concept that applies here.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for the non-clinical tests is based on objective, quantitative measurements of material properties according to recognized international standards (e.g., ISO 6872:2015 for mechanical properties, ISO 10993 series for biocompatibility). For instance, flexural strength is measured directly, rather than being determined by expert consensus or pathology.

8. The sample size for the training set

This question is not applicable. This is a physical device submission demonstrating substantial equivalence to a predicate, not a machine learning or AI device that requires a training set.

9. How the ground truth for the training set was established

This question is not applicable, as there is no training set for this type of device submission.

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Non-Sterile Zirconia Block (Model: Zircos-E ACE) is indicated for the production of artificial teeth in fixed or removable dentures, or for jacket crowns, facings, and veneers.

Non-Sterile Zirconia Block (Model name: Zircos-E ACE) is a manufacture unit that is cut, processed and sintered by CAD/CAM system of the computers designed for production of dental restoration.

The provided document is a 510(k) Summary for a medical device called "Non-Sterile Zirconia Block (Model: Zircos-E ACE)". This document primarily focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing and comparison of technical characteristics, rather than a study proving the device meets specific acceptance criteria for diagnostic or clinical performance in the way an AI/software device would.

Therefore, many of the requested elements for describing "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/software device are not applicable to this submission.

Here's a breakdown of the information available and why certain aspects are not present:

1. Table of Acceptance Criteria and Reported Device Performance:

The document provides a comparison table (Table 1) between the subject device and the predicate device based on various technical characteristics. It asserts "Equivalence" for each characteristic, indicating that the subject device's performance is expected to be comparable and meet similar standards as the predicate. The "acceptance criteria" here are largely implied by the predicate characteristics and compliance with a standard (ISO 6872).

2. Sample size used for the test set and the data provenance:

• Sample Size: Not explicitly stated as a "test set" in the context of clinical or diagnostic performance. For the non-clinical tests (Density, Biocompatibility), the sample sizes used are standard for material testing, but not explicitly detailed in this summary.
• Data Provenance: The tests are non-clinical, conducted according to ISO standards. The origin of the raw material data for these tests is internal to the manufacturer or their testing labs.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. This device is a material (zirconia block) for dental restorations, not a diagnostic or AI device that requires expert ground truth for its performance evaluation. The "ground truth" for material properties is established by physical and chemical testing against established standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. As above, this is for material testing, not a diagnostic study requiring adjudication of expert interpretations.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• Not Applicable. This is a material device, not an AI or software product.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not Applicable. This is a material device, not an AI or software product.

7. The type of ground truth used:

• For the performance characteristics, the "ground truth" is derived from international standard specifications (e.g., ISO 6872 for ceramic materials, ISO 10993 for biocompatibility) and direct quantitative measurements of the material's physical, chemical, and biological properties.

8. The sample size for the training set:

• Not Applicable. This device does not involve a "training set" in the context of machine learning or AI.

9. How the ground truth for the training set was established:

• Not Applicable. As above.

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Non-Sterile Zirconia Block (Model: Finebase, Montblanc, Trione C, Trione HT+) are indicated for the production of artificial teeth in fixed or removable dentures, or for jacket crowns, facings, and veneers.

Non-Sterile Zirconia Block (Model name: Finebase, Montblanc, Trione HT, Trione C, Trione HT+) is a manufacture unit that is cut, processed and sintered by CAD/CAM system of the computers designed for production of dental restoration.

The provided text is a 510(k) Summary for a dental device (Non-Sterile Zirconia Block) and does not describe the acceptance criteria or a study that proves a device (likely an AI/ML-enabled medical device) meets acceptance criteria. The document explicitly states:

"No clinical study is included in this submission." (page 4)

Therefore, I cannot extract the requested information regarding acceptance criteria, performance studies, sample sizes, expert involvement, or adjudication methods from this specific document.

The document focuses on non-clinical tests (material properties and biocompatibility) to demonstrate substantial equivalence to a predicate device, rather than clinical performance (e.g., accuracy of a diagnostic AI).

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